w omen’s health and pregnancy outcomes: do services make...
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Women’s Health and Pregnancy Outcomes:
Do Services Make a Difference?*
March 2001
Elizabeth Frankenberg
Duncan Thomas
*Elizabeth Frankenberg, RAND, 1700 Main Street, Santa Monica, CA 90407; E-mail: [email protected]. Duncan Thomas, RAND and University of Cali fornia at Los Angeles; E-mail: [email protected]. This work was supported by NICHD grants P50HD12639, R29HD32627, and P01HD28372, by NIA grant P30AG12815, and by the POLICY Project. We gratefully acknowledge the comments of Bondan Sikoki, Wayan Suriastini, and participants at seminars at the University of Cali fornia at Los Angeles, the Gadjah Mada University, the University of Maryland, the University of Michigan, the University of Pennsylvania, and the University of Washington.
ABSTRACT
We use data from the Indonesia Family Life Survey to investigate the impact of a major expansion in access to midwifery services on health and pregnancy outcomes for women of reproductive age. Between 1990 and 1998 Indonesia trained some 50,000 midwives. Between 1993 and 1997 these midwives tended to be placed in relatively poor communities that were relatively distant from health centers. We show that additions of vill age midwives to communities between 1993 and 1997 are associated with a significant increase in body mass index in 1997 relative to 1993 for women of reproductive age, but not for men or for older women. The presence of a vill age midwife during pregnancy is also associated with increased birthweight. Both results are robust to the inclusion of community-level fixed effects, a strategy that addresses many of the concerns about biases because of nonrandom program placement.
Decline in mortality is among the most fundamental demographic changes experienced
by developing countries over the past half-century. Today, individuals are leading longer and
healthier li ves than did their parents and grandparents. In part these changes reflect investments
in human resources by both individuals and governments. In virtually every developing country,
governments have built , stocked, and staffed schools, health faciliti es, and family planning
clinics, albeit with varying degrees of success.
Although clinical studies have demonstrated that some health interventions in fact
improve health, researchers have long debated about the contribution of public health
investments to health improvements and mortality decline. Most macro-level studies conclude
that the effect of public spending on health is small (Filmer and Pritchett 1999; Musgrove 1996).
At the micro level, some studies have concluded that investments in providing public health
services have a positive causal effect on health outcomes (Caldwell 1986; Jamison et al. 1993).
The majority of studies, however, indicate that increases in public spending have littl e or no
impact on health; in some cases, public-sector investments are even associated with poorer health
outcomes. (For a discussion, see Strauss and Thomas 1995.)
At least two criti cal problems have plagued this literature. The first, and perhaps the more
diff icult to address, is that public health investments are not likely to be located at random with
respect to health outcomes. For example, if programs are carefully targeted they will be placed
where health outcomes are poor and/or utili zation of services is low. If all program placement
decisions are based on observable characteristics that are controlled in an evaluation of the
program, such targeting poses no conceptual diff iculty. Yet insofar as program placement is
associated with characteristics that are not observed, failure to take account of nonrandom
placement will generally lead to biased estimates of the impact of the investment (Angeles,
Guilkey, and Mroz 1998).
Rosenzweig and Wolpin (1986), for example, show that in a cross-section regression,
children’s nutritional status is negatively associated with exposure to public health programs in
Laguna, The Phili ppines. In contrast, these authors find a positive and significant effect when
they examine how changes in nutritional status respond to changes in exposure to public health
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programs. They attribute the negative correlation in the cross-section estimates to the nonrandom
placement of programs.
A second major stumbling block in this literature is the lack of adequate data, on several
dimensions. Measurement of health investments is not straightforward; this surely contributes to
the weakness of evidence in the macro literature. In the micro literature, the shortcomings of
community-level data on the accessibilit y and quality of health services that can be linked to
individual-level information are well known (Akin, Guilkey, and Denton 1995; Pullum 1991;
Thomas and Maluccio 1996), although recent advances in geographical information systems
have facilit ated the combination of administrative data with sociodemographic surveys (Entwisle
et al. 1997).
Detailed community-level data linked to individual-level data are not always suff icient:
the application of methods that control community- or individual-specific unobservables requires
repeated observations on health outcomes, and very few longitudinal surveys contain that
information on respondents as well as on the health services and other services to which they
have access.
We use data from a new, extremely rich longitudinal survey from Indonesia to evaluate
whether government efforts to provide health care have an impact on the populations targeted by
the programs. Specifically, we consider the Vill age Midwife program, which was initiated in the
1990s and is estimated to have posted some 50,000 midwives throughout the country (Gani
1996; Kosen and Gunawan 1996; Sweet, Tickner, and Maclean 1995). Our goal is to provide
evidence on the effectiveness of this large and important community-based public health service
intervention that is targeted explicitl y to reproductive-age women in underserved communities.
Our results are of general interest because these types of programs have been implemented in
many developing countries.
To measure the effect on health status of the introduction of a new health worker in a
community, we draw on the “quasi-experiment” that occurred in Indonesia by comparing
changes in health status in communities that gained a health worker with such changes in
communities that did not. We recognize that unobserved factors may influence the introduction
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of a health worker to a community, which would cause bias in these “fixed-effects” estimates of
the impact of health workers on health outcomes; thus we take an additional step in the analysis.
Because the health workers are midwives who were trained primarily to serve women of
reproductive age, we contrast the impact on the health of these women (the “treated”) with that
of other adults (the “controls” ) who live in the same community into which the midwife was
introduced.
Our main results focus on the effects of introducing a vill age midwife on a general
measure of adults’ health, the body mass index (BMI). After controlli ng community-level
heterogeneity, we find that among reproductive-age women, BMI increases significantly in
communities that gained a vill age midwife and that the increase is substantively important. In
contrast, men and older women (our “control” groups) do not experience as large an increase in
BMI. For women of reproductive age, the benefits of access to midwives extend to pregnancy
outcomes: we also find that the introduction of a midwife is associated with increases in
birthweight. We conclude that the expansion of the Vill age Midwife program has yielded
significant improvements in health, particularly for women of reproductive age.
BACKGROUND
Notwithstanding the economic crisis of the late 1990s, socioeconomic development in Indonesia
has improved substantially over the past three decades. From 1967 to 1997 Indonesia’s per capita
gross domestic product (GDP) increased by almost 5% per year. At the same time, Indonesia
achieved nearly universal enrollment in primary school and substantial increases in secondary-
school enrollment. Since the early 1960s, several indicators of health status in Indonesia also
have shown major improvements. The infant mortality rate has declined steadily, and by the mid-
1990s li fe expectancy surpassed 60 years.
Maternal mortality, however, had not shown such impressive gains as of the early 1990s,
and the Indonesian government expressed considerable concern about this dimension of health
outcomes. At 390 to 650 deaths per 100,000 live births, this rate was the highest in any of the
ASEAN nations (Handayani et al. 1997; Mukti 1996; UNICEF 2000a, 2000b). In fact, for much
of the 1990s Indonesia’s statistics for maternal mortality were on a par with those in India and
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Bangladesh, even though the per capita GDP in Indonesia was about 50% higher than in India
and about twice as high as in Bangladesh (Sarwono, Mundiharno, and Fortney 1997).
To address poor maternal health, the Ministry of Health (MOH) embarked on an
ambitious program to make midwifery services more widely available by training midwives and
posting them to vill ages throughout Indonesia (Handayani et al. 1997; Kosen and Gunawan
1996; MOH 1994). Between 1990 and 1996 the Government of Indonesia planned to provide a
midwife in every nonmetropolitan vill age or township (MOH 1994). Midwives typically were
recruited from three-year nursing academies and received one additional year of midwifery
training (Sweet et al. 1995). By 1998, 54,000 midwives had been trained; between 1986 and
1996 the number of midwives per 10,000 population increased more than tenfold from 0.2 to 2.6
(Hull et al. 1998; MOH 2000; Reproductive Health Focus 2000).
Once assigned to a community, the midwives are paid a salary by the Government of
Indonesia for three to six years (Hull et al. 1998). They maintain a public practice during normal
working hours and are allowed to practice privately after hours. It is expected that midwives will
build up a client base while working for the government; thus, when their contract ends, they can
maintain their practice in the vill age without a government salary (Gani 1996; MOH 1994).
The role of the vill age midwife, as described by the Indonesian MOH, suggests that she
will affect health status, particularly of reproductive-age women. Her duties include promoting
community participation in health, providing health and family planning services, working with
traditional birth attendants, and referring complicated obstetric cases to health centers and
hospitals. She is to serve as a health resource in her community, actively seeking out patients and
visiting them in their homes rather than waiting passively until they come to her (MOH 1994).
These activities bring a vill age midwife into contact with a wide array of community residents in
a variety of settings, and provide her with opportunities to advise clients on nutrition, food
preparation, sanitation, and other health-promoting behaviors.
Vill age midwives provide general services in addition to those oriented toward maternal
and well -baby care, as supported by research in central Java (Mukti et al. 1997). On the basis of
interviews, record abstraction, and client observations with 19 vill age midwives, the study finds
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that although reproductive-age women are the primary clients, midwives also treat nonobstetric
patients, including men.
Additional evidence about the role of vill age midwives comes from interviews with 157
vill age midwives, which were conducted as part of the Community and Facilit y component of
the Indonesia Family Life Survey (IFLS) in 1997 (described further below). Table 1 summarizes
some of the results from those interviews. In regard to service provision, the vill age midwives
offer their communities more than prenatal care, delivery assistance, and family planning; about
half also provide child immunizations. The great majority of vill age midwives provide more
general curative care, and stitch wounds. About one-third say they can incise and drain
abscesses. Almost all vill age midwives dispense medications such as antibiotics, cough
medicine, vitamins, and supplements of micronutrients such as iron and Vitamin A.
The comprehensiveness of services offered by vill age midwives suggests some of the
pathways through which availabilit y of a vill age midwife may improve health. For example, if a
vill age midwife provides curative care, her presence may reduce durations of ill ness from
diarrheal and respiratory diseases and thus may limit the weight loss associated with such
ill nesses. Because of the midwife’s years of health training and her abilit y to offer an array of
curative and preventive services, coupled with nutrition education and distribution of vitamins
and micronutrients, her arrival in a community may well l ead to improvements in her clients’
nutritional status.
The Vill age Midwife program builds on the public health system of clinics and outreach
activities established in Indonesia during the 1970s and 1980s. The backbone of this system is
the community health center (puskesmas). The health center provides an array of services and is
a basic source of subsidized outpatient care. Health centers generally are headed by a doctor,
who oversees a midwife and various paramedical workers (MOH 1990). In better-off areas the
center’s staff may include several doctors, as well as one or two dentists. Each subdistrict
(kecamatan), consisting of 20 to 40 vill ages or townships, has one or more health centers.
Staff members of the health center, in conjunction with family planning fieldworkers, are
responsible for conducting outreach activities, such as supervision of posyandus (neighborhood
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health posts), within the vill ages and townships in their catchment area. The posyandu is held
monthly and is attended by children under five and their mothers. It is staffed by neighborhood
volunteers and (if possible) by staff members from the health centers or by family planning
fieldworkers. (The latter also provide contraceptive supplies to workers from the health centers
and to posyandus.) When health workers attend, the posts generally provide prenatal care,
immunization, and contraceptive injections (Kosen and Gunawan 1996). When helath workers
do not attend, services are limited to provision of vitamins and oral rehydration solution,
nutritional screening, and oral contraceptives.
Private practitioners also are an important source of health care in Indonesia. Private
services are more widely available in urban than in rural areas, but because employees of the
health center generally offer private services in off -hours, private practitioners are found in rural
areas as well (Brotowasisto et al. 1988; Gani 1996; World Bank 1990).
CONCEPTUAL FRAMEWORK
In Indonesia as in other countries, improvements in health outcomes and expansion in health
services have occurred simultaneously. This fact, however, does not tell us whether the
investments in services caused the improvements in health. It is plausible that other factors that
have changed, including economic growth, are correlated both with improvements in health and
with greater access to services.
In an effort to isolate the role of health services, a number of studies have contrasted
spatial variation in program availabilit y or strength with spatial variation in health outcomes. Yet
a correlation between access and health outcomes at a point in time does not identify the
direction of causality. Services may be provided in a particular location in response to demand
for those services, or people who want services may move to places where they are provided
(Rosenzweig and Wolpin 1986, 1988). Either scenario yields a spurious correlation between
access to services and health outcomes because the relationship is governed by a common
(unobserved) factor.
It is also possible that governments target particular types of communities for
interventions. Targeting will not bias estimates of the effects of the intervention if it is based on
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characteristics that are observed and controlled in a regression context. If targeting is based on
unobserved characteristics, however (or if the full set of characteristics used for targeting is not
controlled in the regression), and if those unobserved characteristics are correlated with the
outcome of interest, estimated effects of the intervention will be biased. The direction of that bias
is ambiguous.
To ill ustrate, imagine that government services are provided in communities that are
underserved by private providers and that health status in those communities is relatively poor,
everything else held equal. Unless all characteristics that underlie the placement of the program
are controlled, the estimated impact of the intervention will be biased negatively, and the bias
will be greatest for the interventions targeted to the people who need them most. This issue of
selective program placement is important in the context of health policies in Indonesia
(Frankenberg 1992; Gertler and Molyneaux 1994; Pitt, Rosenzweig, and Gibbons 1993).
In theory, these complicating issues are sidestepped by social experiments involving
random assignment of subjects to treatment and control groups. Although such experiments have
produced valuable findings regarding some policy questions (see, for example, Berggren,
Ewbank, and Berggren 1981; Dow et al. 1999; Faveau et al. 1991; Newhouse 1994), they have
their own drawbacks. They tend to be small i n scale and to involve homogeneous populations;
thus their generalizabilit y is limited (Ewbank 1994). They are typically expensive, take a long
time to complete, and can be diff icult to implement. In some instances, experiments induce
behavioral responses (such as migration to areas that are served in the trial) that substantially
complicate evaluation of the intervention.
In our view, observational data are an important complement to evaluations of
interventions based on randomized trials. Of course, studies based on observational data cannot
ignore the complicating issues discussed above.
We adopt a quasi-experimental approach to evaluate the effects of an expansion in access
to midwifery services and health outcomes in Indonesia. Using longitudinal household survey
data, we compare an individual’s health before the introduction of a midwife in a community
with the same individual’s health after the intervention. In doing so, we sweep out of the model
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all factors that are fixed at the individual and community level and enter the model additively,
including any fixed characteristics that are correlated with the placement of midwives. This
“ fixed-effects” model has been used extensively in the program evaluation literature (for a
discussion, see Heckman and Robb 1985). We are contrasting changes in health of the “treated”
with changes in health of a control group, namely respondents in communities where midwives
were not introduced: ∆θi = α + βMc + εic , where ∆θi is the change in health of individual i and Mc
is an indicator variable for whether or not a vill age midwife was introduced in community c.
Time-varying unobserved heterogeneity that affects changes in health is captured in εic. The
intercept, α, reflects changes in health of the population between the two waves of the survey
that are not related to the introduction of a midwife. β measures the difference in changes in
health status of those living in communities where a midwife was introduced relative to other
communities. This is an “average treatment effect,” calculated over all people living in the
“ treated” communities.
The Vill age Midwife program was conceived out of concern for maternal mortality.
Because reproductive-age women are likely to benefit most from the introduction of a midwife,
we refine the treatment group to include only those women in the treated communities. We
compare the effect of introducing a midwife on their health with the effect on the health of men
of the same age living in the same communities:
∆θi = α1Iipf + α2Ii
pm + β1Mc * Iipf
+ β2Mc * Ii
pm + εic ,
where Iipf is an indicator variable for prime-age females and Ii
pm is defined analogously for
prime-age males. The coeff icient on the interaction between the prime-age female and midwife
indicator variables, β1, is an estimate of the change in the health of a prime-age woman in a
“ treated” community relative to the change in health of a similar woman in a community where a
midwife was not introduced.
If the introduction of a midwife in a vill age is uncorrelated with time-varying unobserved
heterogeneity, εic , then this model will provide an unbiased estimate of the effect of the program.
Below, however, we show that midwives are more likely to be introduced in poorer communities
with littl e infrastructure. If changes in health differ between poorer and better-off communities,
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β1 will be a biased estimate of the effect of the program. We can gain some sense of the extent of
that bias by comparing changes in health of men in communities where a midwife was
introduced with changes in health of men in other communities. Under the assumption that
midwives have no effect on males’ health, this difference, β2, will be a measure of the “program
placement” effect. The “difference-in-difference” between the effect on females and the effect on
males, β1 – β2, nets out the “program placement” effect and thus provides an estimate of the
“midwife” effect.
It may be that midwives do in fact influence males’ health—directly (through providing
services to men, for example) or indirectly (through spill overs such as nutrition education to
women, which in turn affects men’s health). In this case, the “difference-in-difference” will be a
biased estimate of the impact of introducing a midwife. The empirical importance of this concern
can be probed by expanding the control groups to include older females, Iof, and older males, Iom:
∆θi = α1Iipf + α2Ii
pm + α3Iiof + α4Ii
om + β1Mc * Iipf + β2Mc * Ii
pm (1)
+ β3Mc * Iiof + β4Mc * Ii
om + εic.
Older men are the least likely to benefit directly from the introduction of a midwife. If we
assume that midwives are not detrimental to older men’s health, the difference-in-difference, β1
– β4, provides a lower-bound estimate of the effect of a midwife.1 Older women’s health, on the
other hand, has more in common with that of prime-age women; thus older women may well
benefit from the introduction of a midwife. Therefore we expect that β1 – β3 is li kely to
understate the effect of a midwife.
If the survey measures all the correlates of changes in health status that affect the
allocation of midwives, it is possible to directly estimate the effect of a midwife by controlli ng
those characteristics in the regression. We will experiment with this approach by drawing on the
rich array of community-level information contained in our data source. In addition, the
inclusion of individual- and community-level observables will i ncrease the eff iciency of the
regression estimates.
1Midwives might encourage famili es to reduce their investments in older men’s health, which would bias upward the difference-in-difference results. This strikes us as unlikely, however.
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It is possible, however, that even with controls for observed differences across
communities, the introduction of a midwife is correlated with unobserved heterogeneity, εic,
which would bias estimates of the program’s effect. Thus we include a community-specific fixed
effect, µc; this effect, in a regression of changes in health, ∆θ, serves as a community-specific
time trend and sweeps out all changes that are common across adults in each community that
gained a midwife. The conceptual experiment that we have in mind is to contrast changes in
health of reproductive-age women with changes in health of other adults living in the same
community. Bias due to program placement will be absorbed in the community effect, and we
can estimate the effect of the midwife program. Clearly, in this case, we can estimate only the
difference-in-differences. We exclude the term for prime-age males from the regressions,
∆θi = α1Iipf + α3Ii
of + α4Iiom + β1Mc ∗ Ii
pf + β3Mc ∗ Iiof + β4Mc ∗ Ii
om
Xiγ + µc + εic, (2)
but include individual characteristics, Xi, to improve eff iciency.
The difference-in-differences will be biased if program placement is based on the health
of reproductive-age women relative to the health of other adults in a particular community. We
will explore the evidence for this sort of targeting in the analyses below.
DATA
The data we use for this study come from two rounds of the IFLS, an ongoing panel survey of
individuals, households, communities, and faciliti es. The first round of data (IFLS1, collected in
1993) included interviews with 7,224 households (Frankenberg and Karoly 1995). The IFLS
conducted interviews in 321 enumeration areas in 13 of Indonesia’s 26 provinces, and represents
about 83% of the Indonesian population.2
In 1997 we constructed a resurvey (IFLS2) in which we sought to reinterview all IFLS1
households (and all members of these households in 1997), as well as a set of target members of
IFLS1 households in 1993 who had migrated out by 1997 (Frankenberg and Thomas 2000).
IFLS2 succeeded in reinterviewing 94.5% of IFLS1 households and 92% of the individuals who
2The 321 IFLS enumeration areas are small survey-defined clusters of households located in 312 administrative areas known as desa (vill age) or keluruhan (township), of which there are more than 62,000 in Indonesia. We refer to desa and keluruhan collectively as “vill ages.” For the remainder of this paper we use the term community to designate both an IFLS enumeration area and the larger administrative area (“vill age”) in which it is located.
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were age-eligible for this study. When we condition on observable characteristics (measured in
1993), recontact is slightly higher (0.7%, t = 1.3) in communities that gained a vill age midwife
than in those that did not. We conclude that attrition is not likely to be a source of contamination
in our results.
The IFLS questionnaire covers a broad array of topics. A trained anthropometrist
recorded the height and weight of each household member in both IFLS1 and IFLS2—a central
consideration for this study. Our primary indicator of adults’ health will be body mass index
(BMI), which is weight (in kilograms) divided by height (in meters) squared. BMI is more
directly interpretable than weight (which varies systematically with height); extreme values of
BMI are associated with elevated risk of morbidity, diff iculties in activities of daily li ving, and
mortality (Fogel 1998; Strauss and Thomas 1998; Waaler 1984). BMI also is associated with
physical capacity as indicated by maximal oxygen uptake (Spurr 1983) and labor productivity
(Thomas and Strauss 1997).
Table 2 presents summary statistics of BMI levels for four groups: reproductive-age
women (age 20 to 45 in 1993), men of the same age, older women, and older men. On average,
BMI has increased for prime-age men and women but has remained constant for older
respondents. The table also reports the fraction of each group whose BMI is below 18.5, a cutoff
below which elevated risks of morbidity and mortality are well documented. About 10% of
prime-age adults fall below this cutoff ; this percentage declined between 1993 and 1997. Some
30% of older adults are below the cutoff ; the fraction has increased for older men. In a tiny
fraction of Indonesians, the BMI is high enough to suggest that they are at risk of health
problems from being overweight.3 The regression models are specified in terms of change in
BMI for each respondent; this can be regarded as change in weight for prime-age adults (for
whom height is fixed). We interpret change in BMI as indicating a change in general health
status. Because increases in BMI in the normal range do not have the same implications for
3In 1993 only 4.5% of the sample had a BMI of 28 or higher, the level above which morbidity and mortality have been shown to rise (Fogel 1998; Waaler 1984). Rates are low for each of the demographic groups as well . Among women of reproductive age, 6.7% had a BMI of 28 or higher, as did 6.4% of women 46 and older. Among men, rates were 2% for younger men and 1.9% for older men. In 1997 a total of 6% of respondents had a BMI of 28 or higher.
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health as do increases among those with low BMI, we also present results that focus on
respondents of the latter type.
In part, the changes in BMI reflect changes over the li fe course and changes in diet or
energy expenditure due to changes in availabilit y of household resources. The regressions
control each respondent’s age and education (which are displayed in Table 2) along with
household per capita expenditure (PCE) at the time of the survey. PCE is considered to be a
reliable measure of resource availabilit y in the household.
In this paper we focus on the impact of expanding the Vill age Midwife program. As
clarified in the discussion above, it is important to control for community-level characteristics
that might be correlated both with changes in health and with the introduction of a midwife. The
IFLS is a particularly rich resource in this regard. Each wave of the survey contains a detailed set
of community questionnaires administered in the IFLS enumeration areas. Extensive interviews
are conducted with the head of the vill age or township (or a designated staff member), with the
head of the community women’s group (typically the wife of the head of the vill age), and with
knowledgeable informants in a sample of up to 12 health providers and up to eight schools in the
community. Drawing on those data, we construct measures of other dimensions of the health
service environment and of levels of infrastructure for each wave of the survey.
Table 3 summarizes aspects of the health service environment and the physical
infrastructure environment, as measured by the IFLS1 and IFLS2 community-facilit y surveys.
Access to the Vill age Midwife program is measured with an indicator of whether a vill age
midwife was present in the community in each of the two survey years. Access to health services
is measured as the distance to the health center and to the private practitioner that are closest to
the vill age leader’s off ice. With respect to outreach efforts by health centers, we construct a
variable indicating whether or not the community’s posyandus receive monthly visits from health
center staff members. Physical infrastructure is measured by whether a public phone is located in
the community and whether the community’s main roads are paved.
The IFLS reflects the dramatic expansion of the Vill age Midwife program documented in
the literature on the Indonesian health system. In 1993 just under 10% of IFLS communities had
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a vill age midwife; by 1997 this percentage had increased to 46% (Table 3). Over the four-year
period between survey waves, more than one-third of IFLS communities gained a vill age
midwife.
The data also suggest that one aspect of health centers’ outreach to communities declined
somewhat between 1993 and 1997, as reported by the head of the vill age women’s group. The
fraction of communities reporting that health center staff members visited posyandus in the
community monthly decreased from 96% in 1993 to 88% in 1997. Only about 3% of
communities gained monthly visits to posyandus from health center staff members, while 11% of
communities lost such visits. Possibly in these communities vill age midwives now attend the
posyandu, rendering supervisory visits from health center staff less necessary.
The basic measures of access to public and to private services—distances to the closest
public and private faciliti es as reported by the vill age leader—changed littl e between 1993 and
1997. In 1993 the mean distances to public and to private faciliti es were 1.0 and 0.6 kilometers
respectively. In 1997 the mean distances were 1.1 and 0.5 kilometers. Neither change is
statistically significant. The distance to a health center probably did not change because most of
the expansion in fixed-site government health faciliti es took place before the 1990s. This fact is
helpful in identifying the effect of an expansion in the midwife program.
With respect to physical infrastructure, about half the communities had a public phone in
1997, up from 44% in 1993. Between 1993 and 1997 the fraction of communities in which most
roads are paved increased by 14 percentage points, bringing the total percentage to 84%.
The descriptive statistics indicate a substantial increase in access to vill age midwives
between 1993 and 1997. In examining how these midwives were allocated across communities,
we use the IFLS data from 1993 to explore how aspects of socioeconomic development and
health status, measured at the community level in 1993, are associated with expansion in access
to midwives between 1993 and 1997. The dependent variable in the regressions is a dichotomous
indicator of whether the community gained a vill age midwife between 1993 and 1997. The
results are presented in Table 4.
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In the first model, we include only average per capita expenditure levels of households in
the community (measured in 1993). This model tests whether gaining a vill age midwife varies
with the community’s wealth. Expenditure is specified as a spline with a knot at the 25th
percentile. For communities in the lowest quartile of the expenditure distribution, higher
household expenditure does not affect the probabilit y that a vill age midwife will be assigned to
the community between 1993 and 1997. In contrast, for mean expenditure level in communities
with expenditures in the top three quartiles of the distribution, the coeff icient is large, negative,
and statistically significant. The results provide strong evidence that among the IFLS
communities, the poorest as of 1993 were most likely to gain a vill age midwife by 1997.
In the second specification, we introduce controls for province (coeff icients not shown)
and for other aspects of community infrastructure. The introduction of these additional controls
produces almost a threefold increase in the R2 of the model, from 0.08 to 0.22. Moreover, the
results reveal that the greater a community’s distance from a health center in 1993, the more
likely that community was to gain a vill age midwife by 1997. Distance from a private
practitioner also has a positive but only marginally significant effect. In addition, communities
with a public phone in 1993 were significantly less likely to gain a vill age midwife by 1997.
In the third specification we add controls for per capita expenditure levels in 1997 and for
whether the community’s posyandus received monthly visits from health center staff members in
1997. Because we control simultaneously for these characteristics in 1993, the 1997
characteristics can be regarded as reflecting change since 1993. On the basis of the coeff icients
for the 1997 characteristics, it does not appear that the communities that were becoming poorer
over time were more likely to gain a midwife, or that health centers reduced their outreach
activities in communities that gained a midwife.
In the fourth specification, we introduce a control for the average body mass index of
adults in the community in 1993, as a means of assessing whether health status in the community
is correlated with subsequent introduction of a midwife. The coeff icient on this variable is not
statistically significant. Possibly the BMI of certain demographic groups (rather than of all
15
adults) is correlated with the allocation of vill age midwives. For example, midwives may be
targeted toward communities in which women were particularly disadvantaged.
In the fifth model we add variables measuring the average BMI of men, of women age 50
and above, and of men 50 and above. The coeff icient on mean BMI captures the correlation
between the BMI of prime-age females in 1993 and the introduction of a midwife. Midwives
were more likely to be introduced in communities in which men were heavier than women, and
less likely to be introduced where older women were lighter than prime-age women. On the
margin, the presence of men who are heavy is positively associated with gaining a vill age
midwife, while the presence of older women who are light is negatively associated with gaining
a vill age midwife. Neither of these correlations, however, is significant, and as a group, the BMI
variables are not statistically significant. In the sixth specification we add measures of the
proportion of adults (by age and sex group) whose BMI is less than 18.5, to ascertain whether the
addition of a vill age midwife responds to the prevalence of poor health in 1993 (rather than to an
indicator of average health). None of the coeff icients on these variables is statistically
significant, nor are the health status measures jointly significant. We also tested for a correlation
between mean level of children’s nutritional status in 1993 and receipt of a vill age midwife by
1997, and found no significant relationship between the two.
The community-level measures of health status in 1993 do not appear to predict gaining a
vill age midwife by 1997. Nor does their presence in the models change the relationships of
economic status and of access to infrastructure to gaining a vill age midwife.
In sum, it appears that the increase in the number of vill age midwives between 1993 and
1997 was not a direct response to levels of nutritional status in 1993. Nor was the allocation of
midwives to communities random, however. Instead, the empirical evidence suggests that the
communities into which vill age midwives were introduced between 1993 and 1997 were those
that in 1993 were relatively poor and located far from public health services.
RESULT S
The results presented in Table 4 suggest that a community’s levels of poverty and remoteness
influence whether it received a vill age midwife. If the characteristics that influence receiving a
16
midwife also influence health status, as seems likely, cross-sectional estimates of the relationship
between presence of a vill age midwife and health status will be biased unless the specifications
include controls for all the factors that affect both health status and allocation of vill age
midwives. We address this issue with the strategies described in the conceptual framework,
estimating four models that relate change in BMI to exposure to a vill age midwife. An increase
in BMI over time is interpreted as health-improving. The independent variable of primary
interest is whether the individual li ved in a community that gained a vill age midwife between
1993 and 1997.
Midwives and Adult BMI
Table 5 presents the main regression results. Estimates of β are reported in panels A and B. In
Models 3 and 4 we include controls for individual and household observables. These include
respondent’s education, age, and (at the household level) per capita expenditure. All are specified
as spline functions with several knots to allow flexibly for nonlinearities. Model 3 also includes
other community-level measures such as urban/rural status, gain or loss of monthly visits from
health center staff , changes in distances to health centers and private practices, gain of paved
roads, and gain or loss of a public phone.
We begin with the correlation between the change in an adult’s BMI measured in 1993
and in 1997 and whether a midwife was introduced into the vill age between 1993 and 1997. As
shown in the first column, that correlation is essentially zero.
Following the discussion above, in the second specification (column 2) we refine the
treatment and control groups. Women of reproductive age (20–45 years) are considered the
“ treatment” group and are contrasted with three “control” groups: men in the same age group,
women over 45, and men over 45. This specification allows us to examine the correlations
between gaining a vill age midwife and change in BMI for the four demographic groups, and to
test whether the correlations differ across the groups. (These “difference-in-difference” tests are
presented in panel C.)
The results from this specification indicate that the addition of a vill age midwife to a
community is associated positively with change in BMI for women of reproductive age but
17
negatively for the other demographic groups. The negative correlation is significant for older
men. We do not interpret the negative effects as indicating that midwives hurt everyone except
young women, but rather that these results capture the “program placement” effects; thus they
reflect the fact that midwives are allocated to communities where improvements in health status
are unlikely.
As discussed above, the difference-in-differences address this concern. The pertinent
estimates, reported in panel C, indicate that the presence of a midwife is associated with
significantly improved health in women of reproductive age relative to the health of other
demographic groups.
This result persists when we include observable characteristics of the respondents and
their communities (Model 3), although the differential effect on older and younger women is
slightly smaller (and significant only at 10%). The fact that residence in a community that gained
a vill age midwife is associated with improved BMI only among prime-age women suggests that
the relationship is causal. If placement of vill age midwives occurred in communities where
nutritional status improved for other reasons, one would expect a positive correlation with the
introduction of a vill age midwife for all demographic groups.
Our final specification (Model 4) goes one step further. We include a community-specific
time trend to ask whether, within communities that gained a vill age midwife, the health of
reproductive-age women improved more than that of other adults. The difference-in-difference
estimates (panel C) indicate that the answer is aff irmative in regard to men: BMI improved by
about 0.20 more for reproductive-age women in these communities than for older or younger
men, and these differences are significant. Although the difference is slightly larger for older
men, in keeping with our expectation that spill over benefits of a midwife would be smallest for
this group, the difference between the effect on younger and older men is small and not
significant. Midwives, however, apparently are associated with spill over benefits for older
women: although the latter benefit less than women of reproductive age, that difference-in-
difference is not significant.
18
Inferences drawn from the difference-in-difference results are remarkably consistent
across the three empirical specifications. The evidence suggests that unobserved heterogeneity
contaminates the estimates, particularly among older respondents; thus we are inclined to place
the greatest weight on the estimates in Model 4. Because we are using observations on
individuals at two points in time, we cannot explore dynamics underlying the effect of a midwife
in a community. Rather, in a linear and additive framework, we are measuring the cumulative
effect, by 1997, of a midwife introduced to the community between 1993 and 1997.
If all the gains in BMI associated with the introduction of a vill age midwife accrued to
people with a BMI in the normal range, the benefits of expansion of the vill age midwife would
not be obvious. Therefore we reestimated the models, restricting the sample to respondents
whose BMI was 21 or below in 1993 (roughly half the sample). Panel D of Table 5 reports the
estimated difference-in-differences, which are larger than for the entire sample.4 These results
indicate that individuals with lower BMI benefit more from the introduction of a vill age
midwife.5
The results indicate that increased access to vill age midwives between 1993 and 1997 has
had a positive impact on women’s health, particularly for women of reproductive age. These
effects are greater for women whose BMI was low in 1993. Because no similar effect occurs for
males, we conclude that the effect for women does not reflect placement of midwives in
communities where health would have improved in any case.
We cannot rule out the possibilit y that midwives were placed in communities where
young women’s health would have improved relative to men’s. Although that scenario strikes us
4We prefer this to an alternative specification that focuses on whether a respondent is above or below a particular cutoff point. The 1993 IFLS data contain evidence of a positive association between BMI and greater functioning, better health, and reduced morbidity among people with BMI below 21 (Strauss and Thomas 1998). Moreover, for reproductive-age women at risk of becoming pregnant, a low BMI is a particular disadvantage because it increases the amount of weight they must gain to achieve a healthy pregnancy (Krasovec and Anderson 1991). A discrete outcome would discard much of the information about improvements in health and would tend to bias the estimates toward not finding program effects that exist. 5We also explored whether gaining a vill age midwife particularly benefits women who are similar to the midwife in age and education, as suggested by Rogers and Solomon (1975) with respect to traditional midwives. Our results show that the midwife’s age relative to her client’s has no impact on her effectiveness. Midwives, who themselves are quite well educated, appear to exert a slightly larger effect on the BMI of women with littl e education; this point suggests that socioeconomic similarity between a midwife and her potential clients is not the force governing her effectiveness.
19
as unlikely, we can explore its relevance by assessing whether the timing of the introduction of a
midwife to a community affects women’s reproductive health. Therefore we now contrast the
birthweight of babies born before and after a midwife is introduced into a community.
Midwives and Bir thweight
We use birthweight as a measure of pregnancy outcome. Birthweight is not only a marker of a
successful pregnancy; it also affects the child’s subsequent health. Data from the Phili ppines
have shown that birthweight is correlated both with survival during the neonatal period and with
the risk of stunting in the first two years of li fe (Adair and Guilkey 1997; Popkin et al. 1993). In
both rounds of the IFLS, women were asked to provide detailed accounts of all pregnancies that
occurred in the five years before the survey, including birthweight (if the baby was weighed).
We pool the data from IFLS1 and IFLS2 to obtain information on 5,155 pregnancies (reported by
3,445 women) that occurred between 1988 and 1997 and ended in li ve births.
The mothers reported birthweights for a total of 3,315 births (64% of all births). Mean
birthweight was 3,162 grams; 8.5% of infants were reported as weighing less than 2,500 grams
(the standard cutoff f or low birthweight). Another 6.3% were reported as weighing exactly 2,500
grams. The distribution of reported birthweights in the IFLS data does not suggest unusually
high or low proportions of low-birthweight babies relative to those in other developing countries
or relative to other data from Indonesia (Boerma et al. 1996). We observe heaping on weights (in
kilograms) that end in .0 or .5, as has been observed in other data sets from developing countries
with retrospectively reported birthweight data (Robles and Goldman 1999). The heaping
indicates measurement error in the reported birthweights; such error, for our purposes, will
inflate standard errors but will not bias the estimated effect of a midwife.
We also examined the correlates of reporting a birthweight (results not shown). The
probabilit y that a birthweight is reported increases with the mother’s age (up to age 35) and, as
one might expect, with level of education and with household per capita expenditure.
Birthweights also are much more likely to be reported for first births and for infants delivered
either in a medical setting or at home with the attendance of a biomedically trained assistant than
for infants delivered at home with the assistance of traditional birth attendants. Birthweight is
20
more likely to be reported for more recent births, but there is no association between the
presence of a vill age midwife in the community during the pregnancy and whether a birthweight
was reported. (This finding holds across all communities and in only those communities that had
a vill age midwife by 1997.)
In analyzing the relationship between birthweight and access to a vill age midwife, we
used data from the IFLS2 Community-Facilit y Survey on the number of years a vill age midwife
had been present in the community, combined with information on the time of conception, to
construct a variable indicating whether a vill age midwife was present in the community during
the pregnancy. In communities that had received a vill age midwife by 1997, 63% of pregnancies
occurred before the vill age midwife arrived; 37% occurred after her arrival. This within-
community variation in exposure to the program can be used to estimate the effect of the vill age
midwife’s presence on birthweight, net of aspects of the community that are fixed over time and
affect both the allocation of midwives and pregnancy outcomes.
Table 6 presents results from these fixed-effects analyses of birthweight. The first column
provides the coeff icients for the relationship between birthweight and the presence of a vill age
midwife during the pregnancy, with no controls. Column 2 adds a variety of pregnancy-specific,
mother-specific, and community-specific controls. For each pregnancy we include markers for
whether the pregnancy was the woman’s first and for the infant’s sex, as well as an indicator of
year of birth. We also include measures of the mother’s height, her educational level, and the
(log of) per capita household expenditure. At the community level, we include distance to public
and private health services, whether roads are paved, presence of a public phone, and monthly
visits from health center staff members. Children born before October 1995 were matched to the
1993 community data; those born in October 1995 or later were matched to the 1997 community
data.
In both specifications, birthweights are significantly greater in a community after a
midwife is introduced than before. To attribute this finding to a program placement effect, one
would have to argue that midwives were allocated to areas where birthweight would have
improved even in the absence of midwives; this seems very unlikely.
21
To capture any time trends in birthweight, we also include in Model 2 a term for the year
when the baby was born. It is potentially diff icult to disentangle an effect of time on birthweight
from an effect of the presence of vill age midwife because vill age midwives were phased into
communities over time. Thus, as year of birth increases, so does the probabilit y that a vill age
midwife was present in the community. The coeff icient on year of birth does not indicate
evidence of a significant time trend in birthweights. We also estimated the time trend for
birthweight separately by whether a vill age midwife was ever posted to the community, but the
time trends were not statistically significant for either type of community; nor did the trends
differ from one another.
CONCLUSION
Both the results for change in body mass index and the results for birthweight suggest that
gaining access to a vill age midwife is associated with improvements in health outcomes for
women of reproductive age, and for their babies. The impact of the midwife’s presence on adult
health status is limited to women, primarily those between ages 20 and 45. In communities that
gained a vill age midwife, the change in reproductive-age women’s BMI is significantly larger
than men’s.
For reproductive-age women whose 1993 BMI was 21 or lower, the difference-in-
difference estimates suggest that the addition of a vill age midwife was accompanied by an
increase in BMI equaling at least 0.2. If 0.2 is added to the 1993 BMI of women of reproductive
age, the percentage whose BMI is less than 21 declines from 44% to 41.3% (a decrease of 6%),
while the percentage whose BMI is less than 18.5 declines from 12.8% to 10.9% (a decrease of
nearly 15%).
The estimated effect of gaining a vill age midwife is to increase birthweight by about 80
grams. The fraction of infants who benefit by a gain of 80 grams depends on the range of
weights for which a gain of 80 grams is assumed to improve health. About 8.5% of the babies for
whom weights are reported weighed less than 2,500 grams. It is li kely that all of these infants
would have been at least somewhat better off if they had been 80 grams heavier at birth, even if
they remained below the 2,500-gram threshold for normal birthweight. In addition, a gain of 80
22
grams is li kely to improve the health of the babies whose weight was reported as exactly 2,500
grams (6.3%) and who therefore were at the threshold of normal birthweight, and for babies just
above the threshold but still relatively light.
In this paper we have focused on developing and implementing a statistical strategy for
estimating the size, direction, and statistical significance of the association between access to
vill age midwives and health outcomes. Our results reveal that gaining a vill age midwife has a
effect on the body mass index of women of reproductive age. This effect is larger for women
whose BMI was low in 1993. We also find a small effect on birthweight. These estimates are
robust to several strategies in which we attempt to correct for unobservable characteristics that
might govern both access to midwives and health outcomes; thus they increase our confidence
that a causal mechanism underlies the relationships we observe in the data. For both body mass
index and birthweight, the effects of gaining a vill age midwife are health-improving and
statistically significant. It is li kely that they presage positive effects of the Vill age Midwife
program on a wider array of health behaviors and outcomes.
23
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Table 1. Service Provision by Vill age Midwives
Type of Service Fraction who provide
Maternity Care
Prenatal exam 98% Delivery assistance 91
Tetanus toxoid injection 66
Family Planning
Oral Contraceptives 91
IUD insertions 41
Injectable contraceptives 94
Children’s Immunizations 48
General Care
Curative care: exams, medicine 97
Stitch wounds 76
Incise and drain abscesses 34
Drugs
Antibiotics 96 Cough medicine 94 Oral rehydration solution 93 Iron tablets 95
Vitamin A 84
Note: Based on data from 157 Vill age Midwives interviewed as part of the IFLS2 Community-Facilit y Survey.
27
Table 2. Body Mass Index of Adults: 1993 and 1997
Sex and Age (1993) Sample Size 1993 1997 Change
Women 20-45 3,030 BMI 22.1 22.8 0.7* (0.06) (0.07) (0.03) % BMI<18.5 12.7 9.6 -3.1* (0.6) (0.5) (0.5) Age 37 Education 5.4
Men 20-45 2,232 BMI 21.2 21.6 0.3* (0.06) (0.06) (0.04) % BMI<18.5 12.1 10.7 -1.4 (0.7) (0.7) (0.6) Age 36 Education 6.6
Women 46 and older 1,913 BMI 21.0 21.0 0.0 (0.09) (0.09) (0.04) % BMI<18.5 29.1 28.8 -0.3 (1.0) (1.0) (0.7) Age 61
Education 2.4
Men 46 and older 1,649 BMI 20.4 20.4 0.0 (0.07) (0.08) (0.03) % BMI<18.5 26.9 30.4 3.5* (1.0) (1.0) (0.8) Age 62 Education 4.7
Note: Sample includes 8,824 individuals who were interviewed and measured in both IFLS1 and IFLS2 and were at least 20 years old in IFLS1. Standard errors are in brackets. * indicates that the 1993 and 1997 levels are significantly different from one another (p < .05).
28
Table 3. Access to Health Care and the Health Outreach Programs in 1993 and 1997
1993 1997
% with a Vill age Midwife 9.4% 45.8%*
Gained a Vill age Midwife 36.4
% received monthly visits to posyandu from health center staff 95.6 87.9*
Gained monthly visits 2.8
Lost monthly visits 10.6
Mean distance (km) to the closest health center 1.0 1.1
Mean distance (km) to the closest private practitioner .6 .5
% with a public telephone 44.2 52.0*
Gained a public telephone 12.8
Lost a public telephone 5.0
% with main roads paved 70.7 84.4*
Gained paved (main) roads 13.7
Note: Level of observation is IFLS enumeration area; sample size is 321. * indicates that the 1993 and 1997 statistics are significantly different from one another (p<.05).
29
Table 4. Community-level Corre lates of Gaining a Vill age Midwife by 1997
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 1993 per capita expenditure < 25th % 0.27 1.05 1.53 1.32 1.32 1.07 (spline) (0.78) (0.92) (1.09) (0.94) (0.96) (1.01)
1993 per capita expenditure >= 25th % -1.90** -1.28** -1.00* -1.12* -1.08** -1.00** (spline) (0.39) (0.48) (0.51) (0.48) (0.49) (0.49)
1997 per capita expenditure < 25th % -0.78 (spline) (1.09)
1997 per capita expenditure >= 25th % -0.51 (spline) (0.37)
Mean BMI, 1993 -0.16 -0.24 -0.08 (0.10) (0.21) (0.26)
Mean BMI, males 1993 0.37* 0.27 (0.21) (0.24)
Mean BMI, females > 50 -0.17* -0.18 (0.09) (0.12)
Mean BMI, males > 50 -0.09 -0.02 (0.11) (0.14)
% with BMI < 18.5 3.82 (2.94)
% men with BMI < 18.5 -0.28 (1.93)
% females >=50, with BMI<18.5 -0.22 (1.10)
% males >=50, with BMI<18.5 0.62 (1.10)
Urban residence -0.27 -0.16 -0.21 -0.14 -0.09 (0.36) (0.37) (0.36) (0.37) (0.37)
Distance to nearest health center 0.27** 0.32 0.27** 0.25** 0.25** (0.12) (0.13) (0.13) (0.12) (0.12)
Distance to nearest private practice 0.25* 0.31** 0.24* 0.28** 0.26* (0.13) (0.14) (0.13) (0.14) (0.14)
Monthly visit by Health Ctr Staff (’93) 0.15 0.11 0.22 0.08 0.13 (0.73) (0.74) (0.74) (.74) (0.76)
Monthly visit by Health Ctr Staff (’97) 0.79 (0.52)
Public phone in the community -0.92** -0.84** -0.87* -0.85** -0.76* (0.38) (0.38) (0.38) (0.39) (0.39)
Market in the community -0.03 0.03 0.01 -0.01 0.03 (0.33) (0.33) (0.33) (0.34) (0.35)
Main roads are paved 0.37 0.36 0.33 0.44 0.39 (0.35) (0.36) (0.35) (0.36) (0.37)
Constant -2.79 -11.00 -8.67 10.03 -11.73 -12.07 F 35.05 93.52 98.86 96.97 103.98 106.58 Prob (F) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
R2 0.08 0.22 0.23 0.23 0.25 0.25 χ2 (Health indicators) 9.13 11.81 .06 0.16 Note: Logistic regressions, level of observation is IFLS enumeration area; sample size is 321. Standard errors reported in parentheses. *=p<=.10, ** =p<=.05
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Table 5. Change in BMI between 1993 and 1997 and Gaining a Vill age Midwife Model 1 Model 2 Model 3 Model 4 OLS OLS OLS FE
Covar iates: Individual observables . . Yes Yes Community observables . . Yes . Community specific time trend . . . Yes
A. Gain a Vill age Midwife by 1997 -0.043 (0.035)
B. Gain a Vill age Midwife if: Female 20-45 0.120* 0.112* 0.174* (0.065) (0.066) (0.087) Male 20-45 -0.075 -0.087 . (0.062) (0.064) Female >45 -0.105 -0.059 0.040 (0.076) (0.077) (0.100) Male >45 -0.173** -0.148** -0.032 (0.066) (0.067) (0.103)
C. Difference in Difference: Whole Sample
Treatment Control
Female 20-45 Male 20-45 0.195* 0.199** 0.174** (0.090) (0.089) (0.087) Female >45 0.225** 0.171* 0.134 (0.092) (0.093) (0.093) Male >45 0.293** 0.261** 0.206** (0.098) (0.098) (0.097)
D. Difference in Difference: BMI <= 21
Treatment Control
Female 20-45 Male 20-45 0.212* 0.224** 0.241** (0.114) (0.112) (0.109) Female >45 0.289** 0.302** 0.263** (0.122) (0.120) (0.115) Male >45 0.265** 0.274** 0.210* (0.111) (0.110) (0.114) Note: The dependent variable is the difference between BMI in 1997 and 1993 (BMI in 1997 – BMI in 1993). Difference-in-difference is the differential effect of gaining a midwife on the treatments (females age 20-45) relative to the controls. In Panel C it is based on estimates from Panel B. In Panel D it is based on the subsample with low BMI in 1993. Individual observables include per capita expenditure in 1993 (splines with knots at the quartiles), education (splines with knots at 6 and 9 years), age (splines with knots at 25, 35, 45, 55, and 65 years) all measured at baseline in 1993. Community-level observables include whether the community’s posyandus gained or lost monthly visits from health center staff , whether the community gained paved roads, whether the community gained or lost a public phone, and changes in distances to the closest health center and private practitioner. Sample size is 8,824 adults 20 and older in 1993 for the entire sample. For the last panel (D), where the results are restricted to individuals with BMI<=21, sample size is 4,718. Robust standard errors that permit within-community correlations are reported in parentheses. * = p < .10, ** = p < .05.
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Table 6. Relationship between Bir thweight and Presence of Vill age Midwife Dur ing Pregnancy
Model 1 Model 2
Covar iates: Community fixed effect
Individual observables
Community observables
Yes
•
•
Yes
Yes
Yes
Vill age Midwife present during pregnancy 67.65* 79.50**
(34.49) (40.47)
Year of Birth 1.89
(4.85)
Note: The dependent variable is birthweight measured in grams. Both models include a community-level fixed effect. Presence of a Vill age Midwife during pregnancy is based on information on timing of pregnancy and on arrival date of Vill age Midwife. Individual and community controls are introduced in Model 2. These include first birth, sex, maternal height, maternal age (splines with knots at 25 and 35 years), maternal education (splines with knots at 6 and 9 years), per capita expenditure (splines with knots at the 20th, 50th, and 80th percentile), year of birth, distance to the closest health center, distance to the closest private practitioner, presence of a phone, whether the community’s posyandus receive monthly visits from health center staff , and whether the main roads in the community are paved. Sample size is 3,315 births. Standard errors are included in parentheses. *=p<.10, ** =p<=.05.